A third approach, called noise injection, is to add noise of different levels to the input of the network during training. It has been proved in [1]} that this is equivalent to add Tikhonov regularization to the loss function of the network. As explained in [2]}, this technique improves the stability of the resulting network. Unfortunately, it is not clear to which extent the accuracy of the resulting model is affected by noise injection and, moreover, how much noise should be added to any input to maximize the trade-off between the stability and accuracy.
